可靠的压力识别策略:使用心率变化特征的机器学习方法
Mariam Bahameish1, Tony Stockman2, Jesús Requena Carrión2
1College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
使用心率变化 (HRV) 的机器学习模型可以检测压力. 一个随机森林模型在识别压力与放松方面表现强,即使数据有限,也提高了现实世界的心理健康应用.
科学领域:
- 物理计算的物理计算.
- 机器学习在医疗保健中的应用
- 情感计算是一种情感计算.
背景情况:
- 使用生理数据识别压力,如心率变化 (HRV),对于心理健康干预至关重要.
- 在情感计算和医疗保健研究中,有限的数据集对准确的机器学习 (ML) 模型性能评估提出了挑战.
- 需要强大的ML方法来确保在现实场景中可靠地检测压力.
研究的目的:
- 采用监督学习算法来使用HRV测量对压力和放松状态进行分类.
- 用有限的数据集实施强大的ML策略,包括数据细分,特征选择和模型评估.
- 评估压力识别的ML模型的概括性和解释性.
主要方法:
- 使用监督学习算法来分析心率变化 (HRV) 数据.
- 应用了有限数据集的ML方法建议,包括数据细分和特征选择.
- 模型性能被严格评估,重点是跨独立数据集的概括性.
主要成果:
- 随机森林模型在区分压力和非压力状态方面表现出卓越的表现.
- 该模型在压力与放松之间取得了86.3%的F1得分,在压力与中性状态之间达到65.8%.
- 该模型在独立数据集上表现出普遍性,证实了它区分压力和放松状态的能力.
结论:
- 随机森林模型对使用HRV进行压力识别是有效的,特别是在将压力与放松区分开来.
- 强大的ML方法论,尽管可能比一些研究产生更低的指标,但对于有限数据应用中的概括性和解释性至关重要.
- 这项研究强调了可靠的ML模型对于实际心理健康干预的重要性.
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